The final regularization method we use is inspired from consensus optimization. In a consensus method [1]}, a number of models are independently optimized with their own task-specific
parameters, and the tasks communicate via a penalty that
encourages all the individual solutions to converge around a
common value. Formally, it is defined as
\(M = \Vert \bar{\ell }-\ell _i\Vert ^2\) 
